A new review highlights how machine learning is transforming the way scientists detect and measure organic pollutants in the ...

Tiny particles bounce light around in a unique way, a property that researchers are using to detect pollutants in water and soil samples.

James Cook University researchers are developing a new tool to help farmers monitor crop health and accurately detect diseased sugarcane before it ...

Crop Disease Detection using Machine Learning is a CNN-based system that identifies crop diseases from leaf images and provides preventive measures, helping farmers detect diseases early and reduce ...

A new study shows that machine-learning models can accurately predict daily crop transpiration using direct plant measurements and environmental data. By training models on seven years of ...

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for millions of deaths each year according to the World Health Organization (WHO). Early detection of these diseases ...

Abstract: This paper presents a comprehensive, machine learning-driven solution to enhance agricultural practices by addressing key challenges such as crop yield prediction, pest and disease detection ...

ABSTRACT: Agriculture is essential for humanity’s survival, and productivity is crucial in agriculture. Owing to its multiple nutrients, cherry has become an important fruit for daily consumption; ...

Abstract: In agricultural applications, traditional image and sensor-based methods for plant disease prediction face notable limitations. Image-based approaches often struggle with early-stage ...